Update Example_GeneticAlgorithm.py

Author: ahmedfgad

Example_GeneticAlgorithm.py

@@ -46,6 +46,11 @@
     fitness = GA.cal_pop_fitness(equation_inputs, new_population)
     print("Fitness")
     print(fitness)
+
+    best_outputs.append(numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))
+    # The best result in the current iteration.
+    print("Best result : ", numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))
+    
     # Selecting the best parents in the population for mating.
     parents = GA.select_mating_pool(new_population, fitness, 
                                       num_parents_mating)
@@ -58,7 +63,7 @@
     print("Crossover")
     print(offspring_crossover)
 
-    # Adding some variations to the offsrping using mutation.
+    # Adding some variations to the offspring using mutation.
     offspring_mutation = GA.mutation(offspring_crossover)
     print("Mutation")
     print(offspring_mutation)
@@ -67,10 +72,6 @@
     new_population[0:parents.shape[0], :] = parents
     new_population[parents.shape[0]:, :] = offspring_mutation
 
-    best_outputs.append(numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))
-    # The best result in the current iteration.
-    print("Best result : ", numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))
-
 # Getting the best solution after iterating finishing all generations.
 #At first, the fitness is calculated for each solution in the final generation.
 fitness = GA.cal_pop_fitness(equation_inputs, new_population)